You cannot win the game but you can lose it
While AI can certainly be a competitive advantage, its governance falls under the category of risk management. This means it has little to provide towards winning, but a big blunder can have enormous, potentially existential, impact on a company.
Transparency is key because transparency is instrumental for gaining the public’s trust that AI is applied in a “good” way. Since AI governance offers little in competitive advantage, openness is not going to hurt the bottom line.
Corporations and public debate
Cooperation cannot address AI governance in isolation. Many of the questions need further elaboration by society and public debate. Even apparently simple concepts such as fairness and non-bias are complex and need further discussion.
Internal guidance and external oversight
Companies will need experts and executives, sufficiently empowered, to govern the development of AI and provide concrete guidance to product developers.
While a company’s board of directors may not have the necessary expertise and experience in AI and its implication and risks, a specialized external advisory or supervisory board might be necessary. It will bring in additional expertise, will be independent of company politics and will increase transparency resulting in increased public trust.
Operational vs societal
The impact of AI on society is very broad and many topics still need research and debate. Therefor it might be useful separating between operational topics and societal challenges.
Operational topics are generally understood and typically under company control, e.g. the quality of data, selection of algorithm by their properties, e.g. explainability, or the design of AI enabled solution in a way that the user is in control.
The governess of operational topics can be structured along the following dimensions: data, algorithms, objective functions, policies and user experience.
Societal challenges are much broader, have deeper impact on society and are typically not under a company’s control. Examples include future of work from a economic and a social perspective, wealth distributions and the balance between privacy and safety and security
I will explore these questions in a separate blog post.